Crowd density estimation via multi-scale convolutional neural network in single-image

Abstract

In the field of video surveillance, the use of artificial intelligence to estimate crowd density in public places has been a popular study. In order to improve the accuracy of crowd density estimates, a multi-scale convolution neural network structure is proposed. And the feature fusion of different receptive field information is performed by using multi-column convolution network, and the hierarchical semantic information with different feature maps at different resolutions is merged to generate a crowd density map with higher quality. The experiment was tested on the Shanghaitech dataset, UCF_CC_50 dataset, and WorldExpo’10 dataset with mean absolute error (MAE) and mean square error (MSE) as the evaluation criteria. The results show that the new network model reduce the value MAE and MSE, improving the accuracy of crowd density estimation.